A probabilistic extension to ontology language owl

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A probabilistic extension to ontology language owl (2004)

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by Zhongli Ding , Yun Peng

Venue:	In Proceedings of the 37th Hawaii International Conference On System Sciences (HICSS-37), Big Island
Citations:	112 - 3 self

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@INPROCEEDINGS{Ding04aprobabilistic,
    author = {Zhongli Ding and Yun Peng},
    title = {A probabilistic extension to ontology language owl},
    booktitle = {In Proceedings of the 37th Hawaii International Conference On System Sciences (HICSS-37), Big Island},
    year = {2004}
}

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Abstract

With the development of the semantic web activity, ontologies become widely used to represent the conceptualization of a domain. However, none of the existing ontology languages provides a means to capture uncertainty about the concepts, properties and instances in a domain. Probability theory is a natural choice for dealing with uncertainty. Incorporating probability theory into existing ontology languages will provide these languages additional expressive power to quantify the degree of the overlap or inclusion between two concepts, support probabilistic queries such as finding the most probable concept that a given description belongs to, and make more accurate semantic integration possible. One approach to provide such a probabilistic extension to ontology languages is to use Bayesian networks, a widely used graphic model for knowledge representation under uncertainty. In this paper, we present our on-going research on extending OWL, an ontology language recently proposed by W3C’s Semantic Web Activity. First, the language is augmented to allow additional probabilistic markups, so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into a Bayesian network. Our probabilistic extension to OWL has clear semantics: the Bayesian network obtained will be associated with a joint probability distribution over the application domain. General Bayesian network inference procedures (e.g., belief propagation or junction tree) can be used to compute P(C | e): the degree of the overlap or inclusion between a concept C and a concept represented by a description e. We also provide a similarity measure that can be used to find the most probable concept that a given description belongs to. 1.

Keyphrases

probabilistic extension ontology language language owl bayesian network description belongs owl ontology probable concept semantic web activity probability theory translation rule additional probabilistic markup belief propagation general bayesian network inference procedure natural choice clear semantics language additional expressive power accurate semantic integration graphic model application domain joint probability distribution on-going research support probabilistic query similarity measure junction tree individual concept knowledge representation

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